Metadata can be defined as “data about data.” It is essentially information which describes physical data structures, origins of data, and technical and business rules applied to data during various forms of transformation and movement, for example. A metadata journal provides a service for capturing and recording events which impact metadata and data in a warehouse.
Traditional approaches to metadata management have concentrated on metadata models stored in repositories. These repositories are abstract descriptions of a system which, when browsed or queried, can help the user in understanding the deployed system which it describes.
Metadata management systems built around metadata models and repositories have had a number of problems. In particular, the value of the system largely depends on the extent to which it really describes the deployed system. This can be affected by a number of factors including whether the model was created from the deployed system or vice versa, the detail the model captures about the deployed system, how changes in the deployed system are synchronized with the model, and how the relationship between the deployed system and the model is described and maintained.
In many systems, changes occur which are not captured in the repository, whether for technical or operational reasons. Every such change decreases the correspondence between the model and the deployed system and thus decreases the value of the metadata repository.
In view of the foregoing, there is a need for systems and methods that overcome the limitations of model based metadata solutions.